1. Field of the Invention
This invention pertains to a method and system for the automatic classification of images, more particularly, it pertains to such a method and system for classifying the image, of a scanned object as the image of an object of one of a plurality of predetermined classes of objects Most particularly, this invention relates to such a method and system useful in a missile for classifying a potential target object, such as a ship, as a member of one class of several predetermined classes of ships.
2. Description of the Prior Art
Automatic classification of images is well known in many fields such as the fields of recognition of characters and of target objects. Such classification has received particular attention for distinguishing potential targets or threat objects in the fields of surveillance and missiles In the latter field, it is highly desirable that a missile launching vehicle remain as far as possible from an attacked object so that the vehicle may remain undetected. It is, therefore, highly desirable to provide an image classification system which is suitable for installation in a missile to be launched day or night at a long stand-off range toward a group of potential target objects, such as a fleet of ships, and which serves to identify the most desirable object, such as an aircraft carrier, and signal the guidance system of the missile so that the missile is directed toward this object rather than toward a less desirable target such as a destroyer or a non-combat ship. An image classification system suitable for use in a missile must not only be able to classify an image of a potential target in real or limited time but must be light in weight, compact, and have low power requirements.
It is well known to form the two-dimensional image of an object, as by a raster scan utilized in television and the like, and digitize the amplitude of energy received by a scanner from portions of the object so that the digitized image is, conceptually, a matrix in which the elements or pixels are numbers representing the energy received from corresponding small portions of the object. The received energy may be reflected ambient light or illumination, such as laser light, provided by the scanning system, or may be infrared radiation emitted by the object itself. Such infrared radiation has the desirable properties that it is always available, even at night, and avoids detection of a vehicle, such as a missile carrying the scanning system, by illumination emitted by the vehicle. The two-dimensional image is conceptually similar with different types of energy, but differs since different portions of a target object reflect or emit different energy levels with different types of radiation.
Once the digital image is available it may, in theory, be compared with stored images of classes of objects of interest to determine if the scanned object is one of those classes. However, since the image is represented, typically, by several thousand pixels, an impractically large amount of digital storage memory and time would be required to process the original image. As a result, a limited number of "features" are derived from the image to reduce the storage required. The use of the discrete Fourier transform to reduce the image to a series of coefficients which serve as the derived features is well known for this purpose. This transform is effective, for example, in character recognition in which there are a limited number of pixels, typically less than one hundred. The use of this transform is also well known in range-only-radar (ROR) in which there is obtained initially a one-dimensional vector having at most a few hundred components which represent amplitudes of reflected microwave energy from portions of an object spaced at different distances from a radar device. It is evident that such a vector provides the greatest resolution where an object such as a ship is scanned along its longitudinal axis and that such a vector does not provide a source of as many possible distinguishing features of the object as does a two-dimensional image. In scanning target objects, such as ships, from a moving vehicle, such as an aircraft or a missile, it is apparent that the size of the image derived from an object varies with the range thereto so that, in the past, the use of a transform, such as the Mellin transform, which is scale invariant has been preferred for image classification in this area in contrast to the Fourier transform which has been considered unsuitable as not being scale invariant. In any event, the limited number of pixels or vector components which must be processed in the field of character recognition or range-only-radar greatly simplifies the use of a suitable transform to extract image features.
In the character recognition field it is possible to obtain even illumination of a character. However, in the target recognition field noise from the environment and from movements of the scanned object and scanning vehicle and the like make it difficult to distinguish the pixels of a scanned image representing a potential target from those caused by noise. As a result, it is well known to utilize such well known image preprocessing techniques as a Sobel filter or median filter prior to feature extraction. These preprocessing techniques are, however, based on the relative magnitudes of adjacent pixels rather than on variations of energy received from the scanned object.
After the feature vector representing a scanned object is extracted from the image of the object, the art of image processing provides a number of approaches for determining that the image corresponds to the image of an object in one of several predetermined classes of objects Typically, this determination is made statistically by the use of stored decision rules corresponding individually to the classes. These rules are obtained by training an image classification system with images of objects known to be in each of the classes and deriving the decision rules from feature vectors obtained in the general manner to be used in classifying an unknown object. Typically, as is set forth in well known works on pattern recognition, the vectors from known classes of objects are reduced to decision rules statistically and some suitable classification scheme such as a nearest neighbor classifier or a Bayes classifier is used to compare the feature vector from an unknown object with the derived decision rules.